Optical coherence tomography (OCT) is a noninvasive, depth resolved, volumetric imaging technique that uses principles of interferometry to provide cross-sectional and three-dimensional (3D) imaging of biological tissues. OCT offers millimeter level penetration of tissues with micrometer-scale axial and lateral resolution and is thus well-suited to imaging tissue microstructure. In ophthalmology OCT has become a part of the standard of care and is commonly used to visualize retinal morphology. In recent years OCT methods have been extended to allow visualization of blood flow within tissues and to separate these flow regions from their surrounding tissue microstructure. When applied at the microcirculation level, OCT-based flow imaging techniques are termed “OCT angiography.” Because the retina is organized into distinct layers with separate microvascular beds as well as layers with no vasculature, OCT angiography holds great promise as a clinical tool for analyzing vascular health and pathology in the eye.
OCT angiography (OCT-A) employs the motion of red blood cells (RBCs) as intrinsic contrast, providing high resolution maps of microvascular networks (Wang R et al, Opt Express 15, 4083-4097 (2007); Makita F et al, Opt Express 15, 1271-1283 (2011); incorporated by reference herein). Specifically, OCT angiography algorithms detect decorrelation values in structural OCT signal intensity or phase across pixels over time to separate blood flow from static tissue. Split-spectrum amplitude-decorrelation angiography (SSADA) is an example of an OCT angiography algorithm used in commercial systems. The 3D datasets produced by OCT angiography algorithms have a one-to-one correspondence to the structural OCT images (i.e., they are registered), and therefore, can be used to present the blood flow associated with specific structural features of the tissue under examination. For example, by segmenting the various tissue layers with the 3D dataset and generating 2D en face angiograms (described below), it is possible distinguish between the normal retinal circulation and choroidal circulation, and highlight abnormal neovascularization in the vitreous or outer retina.
A standard method of presenting OCT angiography data is the generation of a 2D en face angiogram, wherein decorrelation data from a three-dimensional “slab” of a given thickness from the dataset is projected onto a 2D plane for viewing (for instance, the voxel set bounded by the inner limiting membrane and the inner nuclear layer may be projected to produce a 2D en face angiogram of the inner retina). These 2D en face angiograms can be interpreted in a manner similar to traditional angiography techniques such as fluorescein angiography (FA) or indocyanine green (ICG) angiography, and are thus well-suited for clinical use. Furthermore, OCT angiography eliminates the risk and reduces the time associated with the dye injections used in FA and ICG procedures (Jia Y et al, Proc Natl Acad Sci USA 112, E2395-2402 (2015); Lopez-Saez M et al, Ann Allergy Asthma Immunol 81, 428-430 (1998); incorporated by reference herein), making it more accessible for clinical use than FA or ICG, and allowing for better visualization of retinal capillaries.
A limitation of OCT angiography, however, is that the visualization of deeper vascular networks is impeded by a shadowgraphic flow projection artifact, which arises from fluctuating shadows cast in the depth direction by flowing blood cells in the more superficial vessels. The shadowgraphic projection results in variation of both amplitude and phase, and can be picked up by most OCT angiography algorithms as false flow, also called projection artifact. On cross-sectional angiograms, the projection artifact appears as elongated flow signals (tails) below blood vessels, which effectively reduces the depth resolution of OCT angiography. On en face angiograms, the projection artifact causes superficial vascular networks to be duplicated on deeper slabs. One clinical problem caused by this artifact is the duplication of normal inner retinal vascular pattern onto the outer retinal slab, which clutters the deeper slab and interferes with the detection and measurement of choroidal neovascularization (CNV) (Braaf K et al, Biomed Opt Express 4, 51-65 (2013); de Carlo T et al, Int J Retina and Vitreous 1, 5 (2015); Dansingani K et al, Eye 29, 703-706 (2015); Spaide R et al, JAMA Ophth 133, 66-73 (2015); Hendargo H et al, Biomed Opt Express 4, 803-821 (2013); Huang Y et al, Biomed Opt Express 6, 1195-1208 (2015); Spaide R et al, Retina 35, 2163-2180 (2015); incorporated by reference herein). Since CNV is the most serious complication of age-related neovascularization (AMD), the leading cause of blindness in the US (Pascolini D et al, Neuro-Ophthalmol 11, 67-115 (2004); EDPR Group, Arch Ophthalmol 122, 477 (2004); Braaf K et al, Biomed Opt Express 4, 51-65 (2013); Ferris F et al, Arch Ophthalmol 102, 1640-1642 (1984); incorporated by reference herein), the flow projection artifact is a problem of great clinical significance.
In commercial OCT angiography and previous work, a slab-subtraction (SS) algorithm has been used to suppress the flow projection artifact. For example, the vascular pattern of the inner retinal circulation can be subtracted from the outer retinal slab, leaving the outer retinal slab vessel-free (as it is known to be in the healthy eye) (Liu L et al, Biomed Opt Express 6, 3564-3576 (2015); Zhang A et al, Biomed Opt Express 6, 4130-4143 (2015); incorporated by reference herein). Unfortunately, the SS algorithm replaces the flow projection artifact with a shadow artifact, the problem of which becomes obvious when one examines a CNV case. The SS algorithm erases most of the CNV that overlaps with the more superficial retinal circulation, leaving gaps that compromise the vascular integrity (i.e., connectivity) and are difficult to reconstruct (Huang D et al, OCT Angiography Atlas, 2015; incorporated by reference herein). Another shortcoming of the SS approach is that it is only operates on 2D en face angiograms; but does not provide correction to cross sectional B-scan images. In other words, the SS algorithm is that it does not suppress flow projection through the depth direction of the slab, leaving obvious tail artifacts on cross-sectional OCT angiograms. Thus, this approach cannot be used to delineate separate vascular plexuses without pre-defining their slab boundaries. Previous histological studies have shown that there are as many as 3 distinct vascular plexuses in the inner retina alone (Chan G et al, Invest Ophthalmol Vis Sci 53, 5502-5514 (2012); incorporated by reference herein). It is difficult to delineate these 3 plexuses in vivo using the SS algorithm. All of these deficiencies underscore the need for a more robust method of processing 3D datasets to remove shadowgraphic flow projection artifact.